ArticleF1000Research2018
iSEE: Interactive SummarizedExperiment Explorer.
Article in F1000Research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
79 citing papers in PubMed.
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- scFlowReport: A Reproducible Workflow for Comparative Downstream Biological Analysis of Single-Cell RNA-seq Data.Biomolecules · 2026Article
- Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.Neuron · 2026Article
- Spatio-molecular gene expression reflects dorsal anterior cingulate cortex structure and function in the human brain.Cell reports · 2026Article
- Yomix: an interactive tool for the exploration of low-dimensional embeddings in omics data.Bioinformatics (Oxford, England) · 2026Article
- Single-cell analysis of signalling and transcriptional responses to type I interferons.EMBO reports · 2026Article
- DeeDeeExperiment: building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor.Bioinformatics (Oxford, England) · 2026Article
- Mapping spatially organized molecular and genetic signatures of schizophrenia across multiple scales in human prefrontal cortex.bioRxiv : the preprint server for biology · 2026Article
- NeuroStream: an interactive platform for exploratory visualization and harmonization of multicohort brain MRI data.Bioinformatics advances · 2026Article
- SCpubr: a user-friendly R-package for generating publication-ready visualizations of single-cell transcriptome analyses.Bioinformatics advances · 2026Article
- Single-cell analysis reveals the cellular and transcriptional heterogeneity of the normal pediatric thyroid.Frontiers in endocrinology · 2026Article
- CIA: unveiling cellular identities with cluster-independent annotation in single-cell RNA sequencing data for comprehensive cell type characterization and exploration.BMC bioinformatics · 2025Article
- Allele-specific immune gene quantification and expression analysis in single-cell RNA-seq data.NAR genomics and bioinformatics · 2025Article
- Article
- Transcriptomic Analysis of the Human Habenula in Schizophrenia.The American journal of psychiatry · 2025Article
- Impact of Alzheimer's disease risk factors and local neuromelanin content on the transcriptomic landscape of the human locus coeruleus.bioRxiv : the preprint server for biology · 2025Article
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.bioRxiv : the preprint server for biology · 2025Article
- Coagulation proteases modulate nucleic acid uptake and cGAS-STING-IFN induction in the tumor microenvironment.JCI insight · 2025Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.